Overview

Dataset statistics

Number of variables30
Number of observations546589
Missing cells0
Missing cells (%)0.0%
Duplicate rows82354
Duplicate rows (%)15.1%
Total size in memory125.1 MiB
Average record size in memory240.0 B

Variable types

Numeric19
Categorical11

Alerts

Dataset has 82354 (15.1%) duplicate rowsDuplicates
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_bookingHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_bookingHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
day_of_week_first_active is highly correlated with day_of_week_account_createdHigh correlation
week_of_year_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
day_of_week_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of_year_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_booking and 1 other fieldsHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_booking and 1 other fieldsHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
day_of_week_first_active is highly correlated with day_of_week_account_createdHigh correlation
week_of_year_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with days_from_first_active_until_booking and 3 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
day_of_week_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of_year_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_bookingHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_bookingHigh correlation
year_first_active is highly correlated with year_first_booking and 1 other fieldsHigh correlation
month_first_active is highly correlated with week_of_year_first_active and 4 other fieldsHigh correlation
day_first_active is highly correlated with day_account_createdHigh correlation
day_of_week_first_active is highly correlated with day_of_week_account_createdHigh correlation
week_of_year_first_active is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_first_booking is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with month_first_active and 4 other fieldsHigh correlation
year_account_created is highly correlated with year_first_active and 1 other fieldsHigh correlation
month_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
day_account_created is highly correlated with day_first_activeHigh correlation
day_of_week_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of_year_account_created is highly correlated with month_first_active and 4 other fieldsHigh correlation
affiliate_channel is highly correlated with affiliate_providerHigh correlation
signup_app is highly correlated with first_device_typeHigh correlation
affiliate_provider is highly correlated with affiliate_channelHigh correlation
first_browser is highly correlated with first_device_typeHigh correlation
first_device_type is highly correlated with signup_app and 1 other fieldsHigh correlation
signup_flow is highly correlated with affiliate_channel and 3 other fieldsHigh correlation
days_from_first_active_until_booking is highly correlated with days_from_account_created_until_first_booking and 5 other fieldsHigh correlation
days_from_account_created_until_first_booking is highly correlated with days_from_first_active_until_booking and 7 other fieldsHigh correlation
year_first_active is highly correlated with days_from_account_created_until_first_booking and 6 other fieldsHigh correlation
month_first_active is highly correlated with year_first_active and 6 other fieldsHigh correlation
day_first_active is highly correlated with day_first_booking and 1 other fieldsHigh correlation
day_of_week_first_active is highly correlated with day_of_week_account_createdHigh correlation
week_of_year_first_active is highly correlated with year_first_active and 6 other fieldsHigh correlation
year_first_booking is highly correlated with days_from_first_active_until_booking and 7 other fieldsHigh correlation
month_first_booking is highly correlated with days_from_first_active_until_booking and 7 other fieldsHigh correlation
day_first_booking is highly correlated with days_from_first_active_until_booking and 6 other fieldsHigh correlation
week_of_year_first_booking is highly correlated with days_from_first_active_until_booking and 9 other fieldsHigh correlation
year_account_created is highly correlated with days_from_account_created_until_first_booking and 6 other fieldsHigh correlation
month_account_created is highly correlated with year_first_active and 6 other fieldsHigh correlation
day_account_created is highly correlated with day_first_active and 1 other fieldsHigh correlation
day_of_week_account_created is highly correlated with day_of_week_first_activeHigh correlation
week_of_year_account_created is highly correlated with year_first_active and 6 other fieldsHigh correlation
affiliate_channel is highly correlated with signup_flow and 3 other fieldsHigh correlation
affiliate_provider is highly correlated with signup_flow and 2 other fieldsHigh correlation
first_affiliate_tracked is highly correlated with affiliate_channel and 1 other fieldsHigh correlation
signup_app is highly correlated with signup_flow and 3 other fieldsHigh correlation
first_device_type is highly correlated with signup_app and 1 other fieldsHigh correlation
first_browser is highly correlated with signup_flow and 2 other fieldsHigh correlation
country_destination is highly correlated with days_from_first_active_until_booking and 4 other fieldsHigh correlation
days_from_first_active_until_account_created is highly skewed (γ1 = 68.33411796) Skewed
signup_flow has 437533 (80.0%) zeros Zeros
days_from_first_active_until_booking has 122273 (22.4%) zeros Zeros
days_from_first_active_until_account_created has 545580 (99.8%) zeros Zeros
days_from_account_created_until_first_booking has 122162 (22.3%) zeros Zeros
day_of_week_first_active has 88730 (16.2%) zeros Zeros
day_of_week_first_booking has 124318 (22.7%) zeros Zeros
day_of_week_account_created has 88683 (16.2%) zeros Zeros

Reproduction

Analysis started2022-02-01 22:34:27.341769
Analysis finished2022-02-01 22:39:49.294876
Duration5 minutes and 21.95 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

signup_flow
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.753039304
Minimum0
Maximum25
Zeros437533
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:49.508357image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile15
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.368695707
Coefficient of variation (CV)3.062507324
Kurtosis11.12837096
Mean1.753039304
Median Absolute Deviation (MAD)0
Skewness3.482994472
Sum958192
Variance28.82289359
MonotonicityNot monotonic
2022-02-01T19:39:49.664001image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0437533
80.0%
125194
 
4.6%
221957
 
4.0%
2511726
 
2.1%
310882
 
2.0%
128554
 
1.6%
246951
 
1.3%
232977
 
0.5%
51984
 
0.4%
41910
 
0.3%
Other values (16)16921
 
3.1%
ValueCountFrequency (%)
0437533
80.0%
125194
 
4.6%
221957
 
4.0%
310882
 
2.0%
41910
 
0.3%
51984
 
0.4%
61906
 
0.3%
71632
 
0.3%
81707
 
0.3%
91570
 
0.3%
ValueCountFrequency (%)
2511726
2.1%
246951
1.3%
232977
 
0.5%
22790
 
0.1%
211023
 
0.2%
20646
 
0.1%
19676
 
0.1%
18634
 
0.1%
17669
 
0.1%
16603
 
0.1%

days_from_first_active_until_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1853
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.4395515
Minimum0
Maximum2228
Zeros122273
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:49.909308image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q3105
95-th percentile675
Maximum2228
Range2228
Interquartile range (IQR)104

Descriptive statistics

Standard deviation241.5053925
Coefficient of variation (CV)2.074083844
Kurtosis9.694032896
Mean116.4395515
Median Absolute Deviation (MAD)6
Skewness2.961521664
Sum63644578
Variance58324.85462
MonotonicityNot monotonic
2022-02-01T19:39:50.194546image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0122273
22.4%
168401
 
12.5%
230714
 
5.6%
319613
 
3.6%
414604
 
2.7%
511272
 
2.1%
610294
 
1.9%
79087
 
1.7%
87969
 
1.5%
96319
 
1.2%
Other values (1843)246043
45.0%
ValueCountFrequency (%)
0122273
22.4%
168401
12.5%
230714
 
5.6%
319613
 
3.6%
414604
 
2.7%
511272
 
2.1%
610294
 
1.9%
79087
 
1.7%
87969
 
1.5%
96319
 
1.2%
ValueCountFrequency (%)
22281
< 0.1%
20012
< 0.1%
19991
< 0.1%
19951
< 0.1%
19921
< 0.1%
19912
< 0.1%
19902
< 0.1%
19801
< 0.1%
19791
< 0.1%
19771
< 0.1%

days_from_first_active_until_account_created
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct326
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2252204124
Minimum0
Maximum1456
Zeros545580
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:50.450860image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1456
Range1456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.1769148
Coefficient of variation (CV)45.18646729
Kurtosis5833.323547
Mean0.2252204124
Median Absolute Deviation (MAD)0
Skewness68.33411796
Sum123103
Variance103.5695948
MonotonicityNot monotonic
2022-02-01T19:39:50.668289image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0545580
99.8%
195
 
< 0.1%
294
 
< 0.1%
478
 
< 0.1%
361
 
< 0.1%
518
 
< 0.1%
614
 
< 0.1%
1312
 
< 0.1%
2910
 
< 0.1%
109
 
< 0.1%
Other values (316)618
 
0.1%
ValueCountFrequency (%)
0545580
99.8%
195
 
< 0.1%
294
 
< 0.1%
361
 
< 0.1%
478
 
< 0.1%
518
 
< 0.1%
614
 
< 0.1%
75
 
< 0.1%
84
 
< 0.1%
98
 
< 0.1%
ValueCountFrequency (%)
14561
< 0.1%
13691
< 0.1%
13611
< 0.1%
11481
< 0.1%
10361
< 0.1%
10111
< 0.1%
10061
< 0.1%
9911
< 0.1%
9841
< 0.1%
9681
< 0.1%

days_from_account_created_until_first_booking
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1968
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.2138627
Minimum-349
Maximum2001
Zeros122162
Zeros (%)22.3%
Negative230
Negative (%)< 0.1%
Memory size4.2 MiB
2022-02-01T19:39:50.899667image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-349
5-th percentile0
Q11
median6
Q3104
95-th percentile673
Maximum2001
Range2350
Interquartile range (IQR)103

Descriptive statistics

Standard deviation241.2466
Coefficient of variation (CV)2.075884877
Kurtosis9.708491478
Mean116.2138627
Median Absolute Deviation (MAD)6
Skewness2.963440907
Sum63521219
Variance58199.92203
MonotonicityNot monotonic
2022-02-01T19:39:51.112520image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0122162
22.3%
168396
 
12.5%
230706
 
5.6%
319600
 
3.6%
414596
 
2.7%
511278
 
2.1%
610303
 
1.9%
79088
 
1.7%
87970
 
1.5%
96321
 
1.2%
Other values (1958)246169
45.0%
ValueCountFrequency (%)
-3491
< 0.1%
-3471
< 0.1%
-3381
< 0.1%
-3081
< 0.1%
-2981
< 0.1%
-2951
< 0.1%
-2881
< 0.1%
-2731
< 0.1%
-2691
< 0.1%
-2611
< 0.1%
ValueCountFrequency (%)
20012
< 0.1%
19991
< 0.1%
19951
< 0.1%
19921
< 0.1%
19912
< 0.1%
19902
< 0.1%
19801
< 0.1%
19791
< 0.1%
19771
< 0.1%
19761
< 0.1%

year_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.712358
Minimum2009
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:51.278043image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2011
Q12012
median2013
Q32013
95-th percentile2014
Maximum2014
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93452385
Coefficient of variation (CV)0.0004643106832
Kurtosis-0.311298521
Mean2012.712358
Median Absolute Deviation (MAD)1
Skewness-0.3969496091
Sum1100126435
Variance0.8733348262
MonotonicityNot monotonic
2022-02-01T19:39:51.437649image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013220917
40.4%
2012155715
28.5%
2014114921
21.0%
201148689
 
8.9%
20106338
 
1.2%
20099
 
< 0.1%
ValueCountFrequency (%)
20099
 
< 0.1%
20106338
 
1.2%
201148689
 
8.9%
2012155715
28.5%
2013220917
40.4%
2014114921
21.0%
ValueCountFrequency (%)
2014114921
21.0%
2013220917
40.4%
2012155715
28.5%
201148689
 
8.9%
20106338
 
1.2%
20099
 
< 0.1%

month_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.845787237
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:51.610152image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.073523256
Coefficient of variation (CV)0.5257672118
Kurtosis-0.8691152425
Mean5.845787237
Median Absolute Deviation (MAD)2
Skewness0.2690725303
Sum3195243
Variance9.446545208
MonotonicityNot monotonic
2022-02-01T19:39:51.807626image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
573322
13.4%
672190
13.2%
460296
11.0%
351509
9.4%
244391
8.1%
843897
8.0%
142469
7.8%
940752
7.5%
733502
6.1%
1132365
5.9%
Other values (2)51896
9.5%
ValueCountFrequency (%)
142469
7.8%
244391
8.1%
351509
9.4%
460296
11.0%
573322
13.4%
672190
13.2%
733502
6.1%
843897
8.0%
940752
7.5%
1031347
5.7%
ValueCountFrequency (%)
1220549
 
3.8%
1132365
5.9%
1031347
5.7%
940752
7.5%
843897
8.0%
733502
6.1%
672190
13.2%
573322
13.4%
460296
11.0%
351509
9.4%

day_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.53396977
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:52.045571image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.483375634
Coefficient of variation (CV)0.5461176867
Kurtosis-1.180670619
Mean15.53396977
Median Absolute Deviation (MAD)7
Skewness-0.008603738253
Sum8490697
Variance71.96766214
MonotonicityNot monotonic
2022-02-01T19:39:52.367708image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2419908
 
3.6%
2219863
 
3.6%
2319801
 
3.6%
319710
 
3.6%
1619576
 
3.6%
1319111
 
3.5%
1519109
 
3.5%
2118991
 
3.5%
2518955
 
3.5%
918934
 
3.5%
Other values (21)352631
64.5%
ValueCountFrequency (%)
113943
2.6%
217015
3.1%
319710
3.6%
418199
3.3%
518502
3.4%
618108
3.3%
717957
3.3%
817629
3.2%
918934
3.5%
1018575
3.4%
ValueCountFrequency (%)
313149
 
0.6%
3011892
2.2%
2915459
2.8%
2817790
3.3%
2718683
3.4%
2618004
3.3%
2518955
3.5%
2419908
3.6%
2319801
3.6%
2219863
3.6%

day_of_week_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.463664655
Minimum0
Maximum6
Zeros88730
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:52.639492image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.763879644
Coefficient of variation (CV)0.7159576856
Kurtosis-0.9596877006
Mean2.463664655
Median Absolute Deviation (MAD)1
Skewness0.2637619426
Sum1346612
Variance3.1112714
MonotonicityNot monotonic
2022-02-01T19:39:52.829133image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2102872
18.8%
1100314
18.4%
390857
16.6%
088730
16.2%
477668
14.2%
559577
10.9%
626571
 
4.9%
ValueCountFrequency (%)
088730
16.2%
1100314
18.4%
2102872
18.8%
390857
16.6%
477668
14.2%
559577
10.9%
626571
 
4.9%
ValueCountFrequency (%)
626571
 
4.9%
559577
10.9%
477668
14.2%
390857
16.6%
2102872
18.8%
1100314
18.4%
088730
16.2%

week_of_year_first_active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.07136258
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:53.028606image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.34587645
Coefficient of variation (CV)0.5544296217
Kurtosis-0.8740536789
Mean24.07136258
Median Absolute Deviation (MAD)10
Skewness0.2717954675
Sum13157142
Variance178.1124182
MonotonicityNot monotonic
2022-02-01T19:39:53.234681image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2318255
 
3.3%
1917720
 
3.2%
2617653
 
3.2%
2117003
 
3.1%
2516966
 
3.1%
2416352
 
3.0%
2016064
 
2.9%
1815936
 
2.9%
2215825
 
2.9%
1714771
 
2.7%
Other values (43)380044
69.5%
ValueCountFrequency (%)
16036
1.1%
26636
1.2%
310094
1.8%
49796
1.8%
59321
1.7%
611131
2.0%
711416
2.1%
811607
2.1%
911654
2.1%
1011560
2.1%
ValueCountFrequency (%)
532
 
< 0.1%
523683
0.7%
515224
1.0%
506410
1.2%
497148
1.3%
486337
1.2%
477444
1.4%
468153
1.5%
457938
1.5%
446734
1.2%

year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.022134
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:53.390736image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12012
median2013
Q32014
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.115754825
Coefficient of variation (CV)0.0005542685331
Kurtosis-0.3775269155
Mean2013.022134
Median Absolute Deviation (MAD)1
Skewness-0.03286890093
Sum1100295755
Variance1.24490883
MonotonicityNot monotonic
2022-02-01T19:39:53.521427image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2013199469
36.5%
2012130447
23.9%
2014114063
20.9%
201559714
 
10.9%
201137742
 
6.9%
20105154
 
0.9%
ValueCountFrequency (%)
20105154
 
0.9%
201137742
 
6.9%
2012130447
23.9%
2013199469
36.5%
2014114063
20.9%
201559714
 
10.9%
ValueCountFrequency (%)
201559714
 
10.9%
2014114063
20.9%
2013199469
36.5%
2012130447
23.9%
201137742
 
6.9%
20105154
 
0.9%

month_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.935291416
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:53.668031image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.799557088
Coefficient of variation (CV)0.4716798034
Kurtosis-0.5787099636
Mean5.935291416
Median Absolute Deviation (MAD)2
Skewness0.2083893141
Sum3244165
Variance7.837519891
MonotonicityNot monotonic
2022-02-01T19:39:53.791711image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6122766
22.5%
566253
12.1%
450446
9.2%
344863
 
8.2%
843064
 
7.9%
740826
 
7.5%
238710
 
7.1%
937789
 
6.9%
131360
 
5.7%
1029336
 
5.4%
Other values (2)41176
 
7.5%
ValueCountFrequency (%)
131360
 
5.7%
238710
 
7.1%
344863
 
8.2%
450446
9.2%
566253
12.1%
6122766
22.5%
740826
 
7.5%
843064
 
7.9%
937789
 
6.9%
1029336
 
5.4%
ValueCountFrequency (%)
1214460
 
2.6%
1126716
 
4.9%
1029336
 
5.4%
937789
 
6.9%
843064
 
7.9%
740826
 
7.5%
6122766
22.5%
566253
12.1%
450446
9.2%
344863
 
8.2%

day_first_booking
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.58152469
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:53.938287image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median17
Q325
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.958689144
Coefficient of variation (CV)0.5402813861
Kurtosis-1.256949852
Mean16.58152469
Median Absolute Deviation (MAD)8
Skewness-0.08979162464
Sum9063279
Variance80.25811117
MonotonicityNot monotonic
2022-02-01T19:39:54.092874image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2967119
 
12.3%
1618084
 
3.3%
518056
 
3.3%
1518024
 
3.3%
1717670
 
3.2%
2317623
 
3.2%
2517590
 
3.2%
1317406
 
3.2%
317321
 
3.2%
2417269
 
3.2%
Other values (21)320427
58.6%
ValueCountFrequency (%)
114121
2.6%
215778
2.9%
317321
3.2%
417184
3.1%
518056
3.3%
616372
3.0%
715881
2.9%
816127
3.0%
917022
3.1%
1016791
3.1%
ValueCountFrequency (%)
312491
 
0.5%
308298
 
1.5%
2967119
12.3%
2813906
 
2.5%
2714872
 
2.7%
2615895
 
2.9%
2517590
 
3.2%
2417269
 
3.2%
2317623
 
3.2%
2217233
 
3.2%

day_of_week_first_booking
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.219698896
Minimum0
Maximum6
Zeros124318
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:54.234086image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.767131882
Coefficient of variation (CV)0.7961133309
Kurtosis-0.954287603
Mean2.219698896
Median Absolute Deviation (MAD)1
Skewness0.3454552419
Sum1213263
Variance3.122755088
MonotonicityNot monotonic
2022-02-01T19:39:54.350779image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0124318
22.7%
297377
17.8%
193945
17.2%
388408
16.2%
472152
13.2%
551602
9.4%
618787
 
3.4%
ValueCountFrequency (%)
0124318
22.7%
193945
17.2%
297377
17.8%
388408
16.2%
472152
13.2%
551602
9.4%
618787
 
3.4%
ValueCountFrequency (%)
618787
 
3.4%
551602
9.4%
472152
13.2%
388408
16.2%
297377
17.8%
193945
17.2%
0124318
22.7%

week_of_year_first_booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.69882672
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:54.507400image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median25
Q333
95-th percentile47
Maximum52
Range51
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.21603757
Coefficient of variation (CV)0.4945999137
Kurtosis-0.6195009967
Mean24.69882672
Median Absolute Deviation (MAD)9
Skewness0.1553782912
Sum13500107
Variance149.2315738
MonotonicityNot monotonic
2022-02-01T19:39:54.749260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2766303
 
12.1%
2417041
 
3.1%
2316357
 
3.0%
2115943
 
2.9%
2515611
 
2.9%
2615402
 
2.8%
1914974
 
2.7%
2014915
 
2.7%
1813839
 
2.5%
2213794
 
2.5%
Other values (42)342410
62.6%
ValueCountFrequency (%)
14267
0.8%
24982
0.9%
37836
1.4%
47929
1.5%
57042
1.3%
68820
1.6%
79669
1.8%
810171
1.9%
910373
1.9%
1010173
1.9%
ValueCountFrequency (%)
522421
 
0.4%
513809
0.7%
504866
0.9%
495606
1.0%
485291
1.0%
476047
1.1%
466404
1.2%
456877
1.3%
445620
1.0%
435899
1.1%

year_account_created
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
2013
220969 
2012
155725 
2014
114960 
2011
48632 
2010
 
6303

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2186356
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2010
3rd row2011
4th row2010
5th row2010

Common Values

ValueCountFrequency (%)
2013220969
40.4%
2012155725
28.5%
2014114960
21.0%
201148632
 
8.9%
20106303
 
1.2%

Length

2022-02-01T19:39:54.917402image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T19:39:55.016102image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
2013220969
40.4%
2012155725
28.5%
2014114960
21.0%
201148632
 
8.9%
20106303
 
1.2%

Most occurring characters

ValueCountFrequency (%)
2702314
32.1%
1595221
27.2%
0552892
25.3%
3220969
 
10.1%
4114960
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2186356
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2702314
32.1%
1595221
27.2%
0552892
25.3%
3220969
 
10.1%
4114960
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common2186356
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2702314
32.1%
1595221
27.2%
0552892
25.3%
3220969
 
10.1%
4114960
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2186356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2702314
32.1%
1595221
27.2%
0552892
25.3%
3220969
 
10.1%
4114960
 
5.3%

month_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.846992896
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:55.126841image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.074223631
Coefficient of variation (CV)0.5257785815
Kurtosis-0.8702109257
Mean5.846992896
Median Absolute Deviation (MAD)2
Skewness0.2683999495
Sum3195902
Variance9.450850933
MonotonicityNot monotonic
2022-02-01T19:39:55.261488image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
573316
13.4%
672140
13.2%
460235
11.0%
351488
9.4%
244380
8.1%
843898
8.0%
142488
7.8%
940746
7.5%
733520
6.1%
1132404
5.9%
Other values (2)51974
9.5%
ValueCountFrequency (%)
142488
7.8%
244380
8.1%
351488
9.4%
460235
11.0%
573316
13.4%
672140
13.2%
733520
6.1%
843898
8.0%
940746
7.5%
1031420
5.7%
ValueCountFrequency (%)
1220554
 
3.8%
1132404
5.9%
1031420
5.7%
940746
7.5%
843898
8.0%
733520
6.1%
672140
13.2%
573316
13.4%
460235
11.0%
351488
9.4%

day_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.53595297
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:55.432988image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.484583055
Coefficient of variation (CV)0.5461256911
Kurtosis-1.181142689
Mean15.53595297
Median Absolute Deviation (MAD)7
Skewness-0.009014229623
Sum8491781
Variance71.98814961
MonotonicityNot monotonic
2022-02-01T19:39:55.596548image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2419936
 
3.6%
2219880
 
3.6%
2319783
 
3.6%
319712
 
3.6%
1619602
 
3.6%
1519106
 
3.5%
1319096
 
3.5%
2118981
 
3.5%
2518964
 
3.5%
918944
 
3.5%
Other values (21)352585
64.5%
ValueCountFrequency (%)
113947
2.6%
217018
3.1%
319712
3.6%
418204
3.3%
518497
3.4%
618107
3.3%
717958
3.3%
817613
3.2%
918944
3.5%
1018593
3.4%
ValueCountFrequency (%)
313148
 
0.6%
3011883
2.2%
2915456
2.8%
2817840
3.3%
2718697
3.4%
2618019
3.3%
2518964
3.5%
2419936
3.6%
2319783
3.6%
2219880
3.6%

day_of_week_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.46403056
Minimum0
Maximum6
Zeros88683
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:55.751760image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.764129836
Coefficient of variation (CV)0.7159529042
Kurtosis-0.9603168888
Mean2.46403056
Median Absolute Deviation (MAD)1
Skewness0.2638651793
Sum1346812
Variance3.112154077
MonotonicityNot monotonic
2022-02-01T19:39:55.863085image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2102937
18.8%
1100355
18.4%
390710
16.6%
088683
16.2%
477648
14.2%
559675
10.9%
626581
 
4.9%
ValueCountFrequency (%)
088683
16.2%
1100355
18.4%
2102937
18.8%
390710
16.6%
477648
14.2%
559675
10.9%
626581
 
4.9%
ValueCountFrequency (%)
626581
 
4.9%
559675
10.9%
477648
14.2%
390710
16.6%
2102937
18.8%
1100355
18.4%
088683
16.2%

week_of_year_account_created
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.07720426
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:56.019710image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q114
median23
Q334
95-th percentile48
Maximum53
Range52
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.34976108
Coefficient of variation (CV)0.5544564449
Kurtosis-0.8753488904
Mean24.07720426
Median Absolute Deviation (MAD)10
Skewness0.2711956554
Sum13160335
Variance178.2161208
MonotonicityNot monotonic
2022-02-01T19:39:56.216653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2318252
 
3.3%
1917704
 
3.2%
2617641
 
3.2%
2117006
 
3.1%
2516963
 
3.1%
2416349
 
3.0%
2016054
 
2.9%
1815934
 
2.9%
2215830
 
2.9%
1714764
 
2.7%
Other values (43)380092
69.5%
ValueCountFrequency (%)
16038
1.1%
26636
1.2%
310095
1.8%
49794
1.8%
59330
1.7%
611135
2.0%
711419
2.1%
811602
2.1%
911652
2.1%
1011557
2.1%
ValueCountFrequency (%)
532
 
< 0.1%
523684
0.7%
515225
1.0%
506411
1.2%
497150
1.3%
486339
1.2%
477484
1.4%
468172
1.5%
457947
1.5%
446755
1.2%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
FEMALE
243928 
MALE
215761 
-unknown-
85272 
OTHER
 
1628

Length

Max length9
Median length6
Mean length5.675562443
Min length4

Characters and Unicode

Total characters3102200
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowFEMALE
3rd rowFEMALE
4th row-unknown-
5th rowFEMALE

Common Values

ValueCountFrequency (%)
FEMALE243928
44.6%
MALE215761
39.5%
-unknown-85272
 
15.6%
OTHER1628
 
0.3%

Length

2022-02-01T19:39:56.383152image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T19:39:56.485385image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
female243928
44.6%
male215761
39.5%
unknown85272
 
15.6%
other1628
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E705245
22.7%
M459689
14.8%
A459689
14.8%
L459689
14.8%
n255816
 
8.2%
F243928
 
7.9%
-170544
 
5.5%
u85272
 
2.7%
k85272
 
2.7%
o85272
 
2.7%
Other values (5)91784
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2334752
75.3%
Lowercase Letter596904
 
19.2%
Dash Punctuation170544
 
5.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E705245
30.2%
M459689
19.7%
A459689
19.7%
L459689
19.7%
F243928
 
10.4%
O1628
 
0.1%
T1628
 
0.1%
H1628
 
0.1%
R1628
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
n255816
42.9%
u85272
 
14.3%
k85272
 
14.3%
o85272
 
14.3%
w85272
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
-170544
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2931656
94.5%
Common170544
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E705245
24.1%
M459689
15.7%
A459689
15.7%
L459689
15.7%
n255816
 
8.7%
F243928
 
8.3%
u85272
 
2.9%
k85272
 
2.9%
o85272
 
2.9%
w85272
 
2.9%
Other values (4)6512
 
0.2%
Common
ValueCountFrequency (%)
-170544
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3102200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E705245
22.7%
M459689
14.8%
A459689
14.8%
L459689
14.8%
n255816
 
8.2%
F243928
 
7.9%
-170544
 
5.5%
u85272
 
2.7%
k85272
 
2.7%
o85272
 
2.7%
Other values (5)91784
 
3.0%

age
Real number (ℝ≥0)

Distinct99
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.41448694
Minimum16
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-02-01T19:39:56.615621image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile23
Q128
median34
Q343
95-th percentile63
Maximum115
Range99
Interquartile range (IQR)15

Descriptive statistics

Standard deviation14.0232941
Coefficient of variation (CV)0.3748092049
Kurtosis5.967223222
Mean37.41448694
Median Absolute Deviation (MAD)6
Skewness2.001328988
Sum20450347
Variance196.6527775
MonotonicityNot monotonic
2022-02-01T19:39:56.787336image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3027920
 
5.1%
3227119
 
5.0%
3127036
 
4.9%
2926706
 
4.9%
2823883
 
4.4%
3423132
 
4.2%
3322934
 
4.2%
2722132
 
4.0%
3520305
 
3.7%
2619312
 
3.5%
Other values (89)306110
56.0%
ValueCountFrequency (%)
1626
 
< 0.1%
1783
 
< 0.1%
183425
 
0.6%
196528
 
1.2%
201562
 
0.3%
215641
 
1.0%
229680
1.8%
2311995
2.2%
2414847
2.7%
2519236
3.5%
ValueCountFrequency (%)
11512
 
< 0.1%
1134
 
< 0.1%
1121
 
< 0.1%
1112
 
< 0.1%
110391
 
0.1%
10935
 
< 0.1%
10815
 
< 0.1%
10723
 
< 0.1%
10625
 
< 0.1%
1056038
1.1%

signup_method
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
basic
343893 
facebook
202184 
google
 
512

Length

Max length8
Median length5
Mean length6.110640719
Min length5

Characters and Unicode

Total characters3340009
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfacebook
2nd rowbasic
3rd rowfacebook
4th rowbasic
5th rowbasic

Common Values

ValueCountFrequency (%)
basic343893
62.9%
facebook202184
37.0%
google512
 
0.1%

Length

2022-02-01T19:39:56.949958image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T19:39:57.051681image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
basic343893
62.9%
facebook202184
37.0%
google512
 
0.1%

Most occurring characters

ValueCountFrequency (%)
b546077
16.3%
a546077
16.3%
c546077
16.3%
o405392
12.1%
s343893
10.3%
i343893
10.3%
e202696
 
6.1%
f202184
 
6.1%
k202184
 
6.1%
g1024
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3340009
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b546077
16.3%
a546077
16.3%
c546077
16.3%
o405392
12.1%
s343893
10.3%
i343893
10.3%
e202696
 
6.1%
f202184
 
6.1%
k202184
 
6.1%
g1024
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin3340009
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b546077
16.3%
a546077
16.3%
c546077
16.3%
o405392
12.1%
s343893
10.3%
i343893
10.3%
e202696
 
6.1%
f202184
 
6.1%
k202184
 
6.1%
g1024
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3340009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b546077
16.3%
a546077
16.3%
c546077
16.3%
o405392
12.1%
s343893
10.3%
i343893
10.3%
e202696
 
6.1%
f202184
 
6.1%
k202184
 
6.1%
g1024
 
< 0.1%

language
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
en
531847 
fr
 
3522
es
 
2482
de
 
2267
zh
 
1742
Other values (20)
 
4729

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1093178
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen

Common Values

ValueCountFrequency (%)
en531847
97.3%
fr3522
 
0.6%
es2482
 
0.5%
de2267
 
0.4%
zh1742
 
0.3%
it1044
 
0.2%
ko903
 
0.2%
ru716
 
0.1%
nl388
 
0.1%
pt358
 
0.1%
Other values (15)1320
 
0.2%

Length

2022-02-01T19:39:57.153370image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en531847
97.3%
fr3522
 
0.6%
es2482
 
0.5%
de2267
 
0.4%
zh1742
 
0.3%
it1044
 
0.2%
ko903
 
0.2%
ru716
 
0.1%
nl388
 
0.1%
pt358
 
0.1%
Other values (15)1320
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e536692
49.1%
n532324
48.7%
r4346
 
0.4%
f3550
 
0.3%
s2848
 
0.3%
d2400
 
0.2%
h1778
 
0.2%
z1742
 
0.2%
t1525
 
0.1%
i1098
 
0.1%
Other values (9)4875
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1093178
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e536692
49.1%
n532324
48.7%
r4346
 
0.4%
f3550
 
0.3%
s2848
 
0.3%
d2400
 
0.2%
h1778
 
0.2%
z1742
 
0.2%
t1525
 
0.1%
i1098
 
0.1%
Other values (9)4875
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin1093178
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e536692
49.1%
n532324
48.7%
r4346
 
0.4%
f3550
 
0.3%
s2848
 
0.3%
d2400
 
0.2%
h1778
 
0.2%
z1742
 
0.2%
t1525
 
0.1%
i1098
 
0.1%
Other values (9)4875
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1093178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e536692
49.1%
n532324
48.7%
r4346
 
0.4%
f3550
 
0.3%
s2848
 
0.3%
d2400
 
0.2%
h1778
 
0.2%
z1742
 
0.2%
t1525
 
0.1%
i1098
 
0.1%
Other values (9)4875
 
0.4%

affiliate_channel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
direct
364733 
sem-brand
72210 
sem-non-brand
47812 
seo
 
23932
other
 
15603
Other values (3)
 
22299

Length

Max length13
Median length6
Mean length6.80928449
Min length3

Characters and Unicode

Total characters3721880
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowseo
2nd rowdirect
3rd rowdirect
4th rowdirect
5th rowother

Common Values

ValueCountFrequency (%)
direct364733
66.7%
sem-brand72210
 
13.2%
sem-non-brand47812
 
8.7%
seo23932
 
4.4%
other15603
 
2.9%
api13812
 
2.5%
content5642
 
1.0%
remarketing2845
 
0.5%

Length

2022-02-01T19:39:57.297028image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T19:39:57.423686image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
direct364733
66.7%
sem-brand72210
 
13.2%
sem-non-brand47812
 
8.7%
seo23932
 
4.4%
other15603
 
2.9%
api13812
 
2.5%
content5642
 
1.0%
remarketing2845
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e535622
14.4%
r506048
13.6%
d484755
13.0%
t394465
10.6%
i381390
10.2%
c370375
10.0%
n229775
6.2%
-167834
 
4.5%
s143954
 
3.9%
a136679
 
3.7%
Other values (7)370983
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3554046
95.5%
Dash Punctuation167834
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e535622
15.1%
r506048
14.2%
d484755
13.6%
t394465
11.1%
i381390
10.7%
c370375
10.4%
n229775
6.5%
s143954
 
4.1%
a136679
 
3.8%
m122867
 
3.5%
Other values (6)248116
7.0%
Dash Punctuation
ValueCountFrequency (%)
-167834
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3554046
95.5%
Common167834
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e535622
15.1%
r506048
14.2%
d484755
13.6%
t394465
11.1%
i381390
10.7%
c370375
10.4%
n229775
6.5%
s143954
 
4.1%
a136679
 
3.8%
m122867
 
3.5%
Other values (6)248116
7.0%
Common
ValueCountFrequency (%)
-167834
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3721880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e535622
14.4%
r506048
13.6%
d484755
13.0%
t394465
10.6%
i381390
10.2%
c370375
10.0%
n229775
6.2%
-167834
 
4.5%
s143954
 
3.9%
a136679
 
3.7%
Other values (7)370983
10.0%

affiliate_provider
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
direct
363730 
google
136487 
other
 
22081
facebook
 
7048
craigslist
 
6073
Other values (12)
 
11170

Length

Max length19
Median length6
Mean length6.050099801
Min length3

Characters and Unicode

Total characters3306918
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgoogle
2nd rowdirect
3rd rowdirect
4th rowdirect
5th rowcraigslist

Common Values

ValueCountFrequency (%)
direct363730
66.5%
google136487
 
25.0%
other22081
 
4.0%
facebook7048
 
1.3%
craigslist6073
 
1.1%
bing5488
 
1.0%
facebook-open-graph1616
 
0.3%
vast1237
 
0.2%
padmapper968
 
0.2%
yahoo766
 
0.1%
Other values (7)1095
 
0.2%

Length

2022-02-01T19:39:57.613178image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
direct363730
66.5%
google136487
 
25.0%
other22081
 
4.0%
facebook7048
 
1.3%
craigslist6073
 
1.1%
bing5488
 
1.0%
facebook-open-graph1616
 
0.3%
vast1237
 
0.2%
padmapper968
 
0.2%
yahoo766
 
0.1%
Other values (7)1095
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e534880
16.2%
r394811
11.9%
t393751
11.9%
i381962
11.6%
c378467
11.4%
d364744
11.0%
o315531
9.5%
g286790
8.7%
l142843
 
4.3%
h24463
 
0.7%
Other values (14)88676
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3303403
99.9%
Dash Punctuation3515
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e534880
16.2%
r394811
12.0%
t393751
11.9%
i381962
11.6%
c378467
11.5%
d364744
11.0%
o315531
9.6%
g286790
8.7%
l142843
 
4.3%
h24463
 
0.7%
Other values (13)85161
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
-3515
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3303403
99.9%
Common3515
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e534880
16.2%
r394811
12.0%
t393751
11.9%
i381962
11.6%
c378467
11.5%
d364744
11.0%
o315531
9.6%
g286790
8.7%
l142843
 
4.3%
h24463
 
0.7%
Other values (13)85161
 
2.6%
Common
ValueCountFrequency (%)
-3515
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3306918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e534880
16.2%
r394811
11.9%
t393751
11.9%
i381962
11.6%
c378467
11.4%
d364744
11.0%
o315531
9.5%
g286790
8.7%
l142843
 
4.3%
h24463
 
0.7%
Other values (14)88676
 
2.7%

first_affiliate_tracked
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
untracked
299592 
linked
119529 
omg
111004 
tracked-other
 
12264
product
 
3839
Other values (2)
 
361

Length

Max length13
Median length9
Mean length7.20114748
Min length3

Characters and Unicode

Total characters3936068
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuntracked
2nd rowuntracked
3rd rowuntracked
4th rowuntracked
5th rowuntracked

Common Values

ValueCountFrequency (%)
untracked299592
54.8%
linked119529
 
21.9%
omg111004
 
20.3%
tracked-other12264
 
2.2%
product3839
 
0.7%
marketing241
 
< 0.1%
local ops120
 
< 0.1%

Length

2022-02-01T19:39:57.792243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T19:39:57.921896image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
untracked299592
54.8%
linked119529
 
21.9%
omg111004
 
20.3%
tracked-other12264
 
2.2%
product3839
 
0.7%
marketing241
 
< 0.1%
local120
 
< 0.1%
ops120
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e443890
11.3%
d435224
11.1%
k431626
11.0%
n419362
10.7%
t328200
8.3%
r328200
8.3%
c315815
8.0%
a312217
7.9%
u303431
7.7%
o127347
 
3.2%
Other values (9)490756
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3923684
99.7%
Dash Punctuation12264
 
0.3%
Space Separator120
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e443890
11.3%
d435224
11.1%
k431626
11.0%
n419362
10.7%
t328200
8.4%
r328200
8.4%
c315815
8.0%
a312217
8.0%
u303431
7.7%
o127347
 
3.2%
Other values (7)478372
12.2%
Dash Punctuation
ValueCountFrequency (%)
-12264
100.0%
Space Separator
ValueCountFrequency (%)
120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3923684
99.7%
Common12384
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e443890
11.3%
d435224
11.1%
k431626
11.0%
n419362
10.7%
t328200
8.4%
r328200
8.4%
c315815
8.0%
a312217
8.0%
u303431
7.7%
o127347
 
3.2%
Other values (7)478372
12.2%
Common
ValueCountFrequency (%)
-12264
99.0%
120
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3936068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e443890
11.3%
d435224
11.1%
k431626
11.0%
n419362
10.7%
t328200
8.3%
r328200
8.3%
c315815
8.0%
a312217
7.9%
u303431
7.7%
o127347
 
3.2%
Other values (9)490756
12.5%

signup_app
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Web
505064 
iOS
 
27965
Moweb
 
7774
Android
 
5786

Length

Max length7
Median length3
Mean length3.070788106
Min length3

Characters and Unicode

Total characters1678459
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWeb
2nd rowWeb
3rd rowWeb
4th rowWeb
5th rowWeb

Common Values

ValueCountFrequency (%)
Web505064
92.4%
iOS27965
 
5.1%
Moweb7774
 
1.4%
Android5786
 
1.1%

Length

2022-02-01T19:39:58.106400image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T19:39:58.240045image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
web505064
92.4%
ios27965
 
5.1%
moweb7774
 
1.4%
android5786
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e512838
30.6%
b512838
30.6%
W505064
30.1%
i33751
 
2.0%
O27965
 
1.7%
S27965
 
1.7%
o13560
 
0.8%
d11572
 
0.7%
M7774
 
0.5%
w7774
 
0.5%
Other values (3)17358
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1103905
65.8%
Uppercase Letter574554
34.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e512838
46.5%
b512838
46.5%
i33751
 
3.1%
o13560
 
1.2%
d11572
 
1.0%
w7774
 
0.7%
n5786
 
0.5%
r5786
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
W505064
87.9%
O27965
 
4.9%
S27965
 
4.9%
M7774
 
1.4%
A5786
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1678459
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e512838
30.6%
b512838
30.6%
W505064
30.1%
i33751
 
2.0%
O27965
 
1.7%
S27965
 
1.7%
o13560
 
0.8%
d11572
 
0.7%
M7774
 
0.5%
w7774
 
0.5%
Other values (3)17358
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1678459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e512838
30.6%
b512838
30.6%
W505064
30.1%
i33751
 
2.0%
O27965
 
1.7%
S27965
 
1.7%
o13560
 
0.8%
d11572
 
0.7%
M7774
 
0.5%
w7774
 
0.5%
Other values (3)17358
 
1.0%

first_device_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Mac Desktop
283506 
Windows Desktop
181885 
iPad
34461 
iPhone
31707 
Other/Unknown
 
5889
Other values (4)
 
9141

Length

Max length18
Median length11
Mean length11.67294439
Min length4

Characters and Unicode

Total characters6380303
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMac Desktop
2nd rowWindows Desktop
3rd rowMac Desktop
4th rowMac Desktop
5th rowMac Desktop

Common Values

ValueCountFrequency (%)
Mac Desktop283506
51.9%
Windows Desktop181885
33.3%
iPad34461
 
6.3%
iPhone31707
 
5.8%
Other/Unknown5889
 
1.1%
Desktop (Other)3489
 
0.6%
Android Phone2952
 
0.5%
Android Tablet2623
 
0.5%
SmartPhone (Other)77
 
< 0.1%

Length

2022-02-01T19:39:58.380666image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-01T19:39:58.545255image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
desktop468880
45.9%
mac283506
27.8%
windows181885
 
17.8%
ipad34461
 
3.4%
iphone31707
 
3.1%
other/unknown5889
 
0.6%
android5575
 
0.5%
other3566
 
0.3%
phone2952
 
0.3%
tablet2623
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o696965
10.9%
s650765
10.2%
e515694
 
8.1%
t481035
 
7.5%
k474769
 
7.4%
474532
 
7.4%
D468880
 
7.3%
p468880
 
7.3%
a320667
 
5.0%
M283506
 
4.4%
Other values (20)1544610
24.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4865663
76.3%
Uppercase Letter1027087
 
16.1%
Space Separator474532
 
7.4%
Other Punctuation5889
 
0.1%
Open Punctuation3566
 
0.1%
Close Punctuation3566
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o696965
14.3%
s650765
13.4%
e515694
10.6%
t481035
9.9%
k474769
9.8%
p468880
9.6%
a320667
6.6%
c283506
5.8%
i253628
 
5.2%
n239863
 
4.9%
Other values (7)479891
9.9%
Uppercase Letter
ValueCountFrequency (%)
D468880
45.7%
M283506
27.6%
W181885
 
17.7%
P69197
 
6.7%
O9455
 
0.9%
U5889
 
0.6%
A5575
 
0.5%
T2623
 
0.3%
S77
 
< 0.1%
Space Separator
ValueCountFrequency (%)
474532
100.0%
Other Punctuation
ValueCountFrequency (%)
/5889
100.0%
Open Punctuation
ValueCountFrequency (%)
(3566
100.0%
Close Punctuation
ValueCountFrequency (%)
)3566
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5892750
92.4%
Common487553
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o696965
11.8%
s650765
11.0%
e515694
8.8%
t481035
 
8.2%
k474769
 
8.1%
D468880
 
8.0%
p468880
 
8.0%
a320667
 
5.4%
M283506
 
4.8%
c283506
 
4.8%
Other values (16)1248083
21.2%
Common
ValueCountFrequency (%)
474532
97.3%
/5889
 
1.2%
(3566
 
0.7%
)3566
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6380303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o696965
10.9%
s650765
10.2%
e515694
 
8.1%
t481035
 
7.5%
k474769
 
7.4%
474532
 
7.4%
D468880
 
7.3%
p468880
 
7.3%
a320667
 
5.0%
M283506
 
4.4%
Other values (20)1544610
24.2%

first_browser
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Chrome
191033 
Safari
134561 
Firefox
98407 
IE
45387 
Mobile Safari
41623 
Other values (36)
35578 

Length

Max length18
Median length6
Mean length6.602147134
Min length2

Characters and Unicode

Total characters3608661
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowChrome
2nd rowIE
3rd rowFirefox
4th rowChrome
5th rowSafari

Common Values

ValueCountFrequency (%)
Chrome191033
35.0%
Safari134561
24.6%
Firefox98407
18.0%
IE45387
 
8.3%
Mobile Safari41623
 
7.6%
-unknown-30549
 
5.6%
Chrome Mobile2105
 
0.4%
Android Browser1126
 
0.2%
Opera402
 
0.1%
AOL Explorer294
 
0.1%
Other values (31)1102
 
0.2%

Length

2022-02-01T19:39:58.786580image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chrome193138
32.6%
safari176184
29.8%
firefox98450
16.6%
ie45400
 
7.7%
mobile43786
 
7.4%
unknown30549
 
5.2%
browser1229
 
0.2%
android1126
 
0.2%
opera406
 
0.1%
explorer314
 
0.1%
Other values (33)1452
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r472896
13.1%
o369409
10.2%
a353145
9.8%
e337923
9.4%
i320119
8.9%
f274634
 
7.6%
m193616
 
5.4%
h193441
 
5.4%
C193424
 
5.4%
S176438
 
4.9%
Other values (36)723616
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2894250
80.2%
Uppercase Letter607855
 
16.8%
Dash Punctuation61098
 
1.7%
Space Separator45445
 
1.3%
Other Punctuation13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r472896
16.3%
o369409
12.8%
a353145
12.2%
e337923
11.7%
i320119
11.1%
f274634
9.5%
m193616
6.7%
h193441
6.7%
x98844
 
3.4%
n93004
 
3.2%
Other values (14)187219
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
C193424
31.8%
S176438
29.0%
F98470
16.2%
E45714
 
7.5%
I45516
 
7.5%
M44095
 
7.3%
A1537
 
0.3%
B1390
 
0.2%
O702
 
0.1%
L294
 
< 0.1%
Other values (9)275
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-61098
100.0%
Space Separator
ValueCountFrequency (%)
45445
100.0%
Other Punctuation
ValueCountFrequency (%)
.13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3502105
97.0%
Common106556
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r472896
13.5%
o369409
10.5%
a353145
10.1%
e337923
9.6%
i320119
9.1%
f274634
7.8%
m193616
 
5.5%
h193441
 
5.5%
C193424
 
5.5%
S176438
 
5.0%
Other values (33)617060
17.6%
Common
ValueCountFrequency (%)
-61098
57.3%
45445
42.6%
.13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3608661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r472896
13.1%
o369409
10.2%
a353145
9.8%
e337923
9.4%
i320119
8.9%
f274634
 
7.6%
m193616
 
5.4%
h193441
 
5.4%
C193424
 
5.4%
S176438
 
4.9%
Other values (36)723616
20.1%

country_destination
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
NDF
54850 
GB
52706 
ES
50512 
US
47693 
NL
47593 
Other values (7)
293235 

Length

Max length5
Median length2
Mean length2.346369942
Min length2

Characters and Unicode

Total characters1282500
Distinct characters20
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNDF
2nd rowUS
3rd rowother
4th rowUS
5th rowUS

Common Values

ValueCountFrequency (%)
NDF54850
10.0%
GB52706
9.6%
ES50512
9.2%
US47693
8.7%
NL47593
8.7%
PT47100
8.6%
other44824
8.2%
FR43911
8.0%
CA42544
7.8%
IT40213
7.4%
Other values (2)74643
13.7%

Length

2022-02-01T19:39:59.012487image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ndf54850
10.0%
gb52706
9.6%
es50512
9.2%
us47693
8.7%
nl47593
8.7%
pt47100
8.6%
other44824
8.2%
fr43911
8.0%
ca42544
7.8%
it40213
7.4%
Other values (2)74643
13.7%

Most occurring characters

ValueCountFrequency (%)
N102443
 
8.0%
F98761
 
7.7%
S98205
 
7.7%
D92689
 
7.2%
E88351
 
6.9%
T87313
 
6.8%
U84497
 
6.6%
A79348
 
6.2%
B52706
 
4.1%
G52706
 
4.1%
Other values (10)445481
34.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1058380
82.5%
Lowercase Letter224120
 
17.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N102443
9.7%
F98761
9.3%
S98205
9.3%
D92689
8.8%
E88351
 
8.3%
T87313
 
8.2%
U84497
 
8.0%
A79348
 
7.5%
B52706
 
5.0%
G52706
 
5.0%
Other values (5)221361
20.9%
Lowercase Letter
ValueCountFrequency (%)
o44824
20.0%
t44824
20.0%
h44824
20.0%
e44824
20.0%
r44824
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1282500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N102443
 
8.0%
F98761
 
7.7%
S98205
 
7.7%
D92689
 
7.2%
E88351
 
6.9%
T87313
 
6.8%
U84497
 
6.6%
A79348
 
6.2%
B52706
 
4.1%
G52706
 
4.1%
Other values (10)445481
34.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1282500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N102443
 
8.0%
F98761
 
7.7%
S98205
 
7.7%
D92689
 
7.2%
E88351
 
6.9%
T87313
 
6.8%
U84497
 
6.6%
A79348
 
6.2%
B52706
 
4.1%
G52706
 
4.1%
Other values (10)445481
34.7%

Interactions

2022-02-01T19:39:29.962653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:26.515135image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:37.308758image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:51.069752image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:02.490413image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:14.173929image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:24.509370image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:34.463397image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:44.323101image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:55.366707image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:05.393985image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:14.124885image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:23.562625image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:33.390533image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:42.895409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:53.583572image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:01.984374image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:12.193853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:21.363669image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:30.493353image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:27.107987image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:37.826372image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:51.603323image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:02.967138image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:14.649655image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:24.988087image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:35.313008image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:45.280051image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:55.779450image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:05.809317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:14.696050image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:23.965315image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:33.980725image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:43.333574image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:54.198924image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:02.571437image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:12.584925image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:21.884284image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:30.959672image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:27.639600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:38.392550image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:52.656018image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:03.755618image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:15.338687image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:25.414954image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:35.744065image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:46.322319image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:56.191541image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:06.241317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:15.382946image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:24.525260image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:34.468319image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:43.817895image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:54.647639image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:03.173320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:12.977652image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:22.397422image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:31.512137image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:28.146755image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:39.020591image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:53.553615image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:04.716886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:15.902175image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:25.869735image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2022-02-01T19:39:27.244037image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:38.122428image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:33.863291image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:47.588929image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:00.146625image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:10.605700image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:21.974011image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:31.993728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:41.160919image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:53.031779image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:03.138442image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:11.721993image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:21.412923image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:30.181955image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:40.505590image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:50.651986image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:59.696751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:10.109238image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:18.012977image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:27.660658image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:38.686109image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:34.421670image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:48.458801image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:00.615370image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:11.328760image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:22.416985image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:32.427575image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:41.576348image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:53.471600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:03.597213image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:12.164357image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:21.829765image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:30.784369image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:40.948443image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:51.456707image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:00.238386image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:10.511687image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:18.750068image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:28.072553image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:39.265072image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:35.674588image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:49.351507image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:01.051203image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:12.175280image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:22.858800image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:32.857423image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:42.070807image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:53.899454image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:04.035041image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:12.608249image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:22.248566image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:31.403860image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:41.407218image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:51.873089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:00.666246image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:10.922965image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:19.429712image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:28.483081image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:39.773707image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:36.245086image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:49.969388image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:01.479060image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:12.942226image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:23.359462image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:33.373652image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:42.736027image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:54.431762image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:04.459628image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:13.106232image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:22.680854image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:31.996478image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:41.944754image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:52.371016image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:01.076092image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:11.331346image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:20.038246image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:28.883258image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:40.292464image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:36.821085image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:36:50.639902image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:01.967749image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:13.636367image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:23.947949image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:33.966667image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:43.592059image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:37:54.945768image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:04.967613image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:13.622184image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:23.162566image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:32.809089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:42.470412image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:38:53.034249image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:01.539629image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:11.794810image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:20.764808image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-02-01T19:39:29.442020image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2022-02-01T19:39:59.233895image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-01T19:39:59.735376image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-01T19:40:00.173489image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-01T19:40:00.564449image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-01T19:40:00.832772image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-01T19:39:41.313091image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-01T19:39:44.001630image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

signup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of_week_account_createdweek_of_year_account_createdgenderagesignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination
0022287321496200952352120156290272011525221MALE38facebookenseogoogleuntrackedWebMac DesktopChromeNDF
13419476-572009691242010820312010928139FEMALE56basicendirectdirectuntrackedWebWindows DesktopIEUS
201043765278200910315442012985362011125049FEMALE42facebookendirectdirectuntrackedWebMac DesktopFirefoxother
3072280-20820091281502010218372010914137-unknown-41basicendirectdirectuntrackedWebMac DesktopChromeUS
4030320101255320101511201012553FEMALE46basicenothercraigslistuntrackedWebMac DesktopSafariUS
5010010201013653201011322201013653FEMALE47basicendirectdirectomgWebMac DesktopSafariUS
60206020620101401201072933020101401FEMALE50basicenothercraigslistuntrackedWebMac DesktopSafariUS
70000201014012010140120101401-unknown-46basicenothercraigslistomgWebMac DesktopFirefoxUS
80202201014012010162120101401FEMALE36basicenothercraigslistuntrackedWebMac DesktopFirefoxUS
9020010200120101511201562902720101511FEMALE47basicenothercraigslistuntrackedWebiPhone-unknown-NDF

Last rows

signup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of_week_account_createdweek_of_year_account_createdgenderagesignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination
5465790101201312171512013121825120131217151MALE31facebookendirectdirectlinkedWebMac DesktopSafariother
546580207307320142528201441851820142528FEMALE30basicendirectdirectuntrackediOSMac DesktopChromeother
5465810000201311191472013111914720131119147MALE24basiczhdirectdirectlinkedWebWindows DesktopChromeother
5465820101201310114020131022402013101140FEMALE41basicendirectdirectomgWebMac DesktopSafariother
5465830101201332131220133224122013321312FEMALE33basicendirectdirectlinkedWebMac DesktopFirefoxother
5465840000201342101720134210172013421017-unknown-41basicensem-brandgoogleomgWebWindows DesktopChromeother
5465850550552012720272012826534201272027MALE28facebookensem-brandgoogleomgWebMac DesktopChromeother
546586022022201452122120146123242014521221-unknown-30basicendirectdirectuntrackedWebWindows DesktopIEother
5465870202201411303201411523201411303FEMALE30basicendirectdirectlinkedWebMac DesktopSafariother
5465880101201272052920127220302012720529MALE25facebookendirectdirectuntrackedWebWindows DesktopChromeother

Duplicate rows

Most frequently occurring

signup_flowdays_from_first_active_until_bookingdays_from_first_active_until_account_createddays_from_account_created_until_first_bookingyear_first_activemonth_first_activeday_first_activeday_of_week_first_activeweek_of_year_first_activeyear_first_bookingmonth_first_bookingday_first_bookingday_of_week_first_bookingweek_of_year_first_bookingyear_account_createdmonth_account_createdday_account_createdday_of_week_account_createdweek_of_year_account_createdgenderagesignup_methodlanguageaffiliate_channelaffiliate_providerfirst_affiliate_trackedsignup_appfirst_device_typefirst_browsercountry_destination# duplicates
76270000201366323201366323201366323FEMALE25basicendirectdirectuntrackedWebMac DesktopSafariPT128
8330000201231292012312920123129FEMALE40basicensem-brandgooglelinkedWebMac DesktopFirefoxPT127
79110000201361552420136155242013615524FEMALE31facebookendirectdirectuntrackedWebWindows DesktopChromePT122
46200000201312544201312554201312544FEMALE25facebookendirectdirectlinkedWebMac DesktopSafariPT108
131920000201462302620146230262014623026FEMALE21facebookenseofacebookuntrackedWebMac DesktopChromePT97
75550000201363123201363123201363123FEMALE19basicendirectdirectuntrackedWebMac DesktopSafariPT96
83650000201362732620136273262013627326FEMALE45basicensem-non-brandgoogleomgWebWindows DesktopIEPT96
54000000201333010201333010201333010-unknown-35basicensem-brandgoogleomgWebWindows DesktopChromePT95
18100000201261912520126191252012619125FEMALE37facebookendirectdirectomgWebMac DesktopSafariPT92
54760000201335110201335110201335110MALE25facebookensem-brandgoogleomgWebWindows DesktopChromePT86